Research

My research aims to develop machine learning systems that are adaptable, robust and energy‑efficient. I investigate continual and domain‑incremental learning methods, robust and secure ML techniques, federated and distributed learning algorithms, and multimodal perception systems. By combining systems and algorithmic perspectives, I build models that generalize across different devices, domains and over time, while maintaining high performance under constraints like limited data, hardware resources and adversarial conditions.

Some of my current interests include:

• Continual learning frameworks to mitigate catastrophic forgetting.
• Robust and secure ML systems resilient to data poisoning and adversarial attacks.
• Federated and distributed learning methods for resource‑constrained devices.
• Energy‑efficient and multimodal perception systems for edge and robotics applications.

Publications

• Overcoming Catastrophic Forgetting with Continual Learning for Smartphone‑based Indoor Localization – Introduced the first path‑based class‑incremental learning framework for localization. IEEE Access, 2025.
• Domain‑Incremental Learning for Indoor Localization using Smartphones – Proposed a novel domain‑incremental framework addressing temporal and device‑induced shifts. IPIN Conference, 2025.
• Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization – Developed a gradient‑saliency based adversarial detection mechanism. DATE, 2025.
• Unified Class and Domain Incremental Learning with Mixture‑of‑Experts for Indoor Localization – Developed a domain‑incremental VAE‑driven representation learning framework. DATE, 2026.

Experience

• Applied AI/ML Research Solutions Intern – Nokia Bell Labs (Jun 2025 – Aug 2025)
  Developed continual object detection systems extending YOLOv7/YOLOv8 with regularization and knowledge distillation; optimized for edge deployment with low storage and fast inference; contributed to multimodal sensing for indoor robotics.
• Graduate Research Assistant – CSU Vice President of Research (Mar 2024 – May 2025, Sep 2025 – Present)
  Supported ML researchers in distributed training, Slurm optimization and GPU profiling; built automated workflows and reproducible environments; developed tools for parallel training, profiling and dataset preprocessing on HPC hardware.
• Senior Systems Engineer – Infosys Ltd. (Jan 2021 – Dec 2022)
  Engineered CI/CD pipelines for large‑scale C++ systems; developed Go benchmark suites for server performance profiling; created a Node.js‑based telemetry and monitoring system for production workloads.

Projects

• Adversarial Data Poisoning Attacks in NL‑to‑Code Generation – Investigated targeted and untargeted poisoning attacks on NL‑to‑code LLMs (CodeT5+, CodeBERT, StarCoder) to reveal robustness gaps and defensive strategies.
• Federated Learning for Indoor Localization – Achieved 7.8× accuracy improvement and 2.1× latency reduction using fused models and saliency-driven aggregation.
• Continual Learning for Indoor Localization – Enhanced accuracy by 58.6%, reduced storage by 74.9%, and decreased energy use by 69.53% via continual learning strategies.
• Dog Emotion Detection (Edge ML System) – Deployed a CNN on Raspberry Pi using TFLite for real‑time emotion classification with 83% accuracy.

Contact

Email: akhil.singampalli@colostate.edu

Location: Fort Collins, CO, United States

If you would like to collaborate or learn more about my research, feel free to reach out via email.